• What is data wrangling?
• Tidyverse package + pipe operator
• Tidying data
  • Renaming, dates and times, pivot longer and wider
• Data manipulation
  • Head, tail
  • Filter, select, arrange
  • Grouping, summarizing, mutating

• How to tell a story with data
  • 5 steps of data analysis
• Introduction into visualization
• Visualization in R
  • Base R graphs
  • Introduction into ggplot

1. Ask
2. Collect and understand
3. Clean
4. Analyze
5. Tell your story

1. What is the question your research will answer?
2. Why is your data interesting?
3. What do you think will be interesting things to show?
4. Hypothesize: what story do I expect my data will tell
5. What tables/figures do I think might be interesting to create? (without having looked at the data)

1. Collect the data
2. Understand how the data is generated (very precisely)
3. Understand what all the variables really mean
4. Ask questions to yourself to check whether you fully understand every aspect of your data
5. Visualize your data to get a feeling of what stories the data might tell

1. Import the data into the program you will use for analyzing - R
2. Perform checks on the data (missing values, outliers, summary statistics)
3. Question the outcome of all your checks, does any cleaning need to be done?
4. Clean the data based on the careful analysis you did
5. Make sure the data is in the correct format for you to analyze

1. Step away from your data and write a list of tables you want to create - Practical
2. Step away from your data and draw rough sketches of the figures you want to create - Practical
3. Do the first step data analysis: create simple summary statistics, as well as the first and easiest tables and figures - Practical
4. Identify patterns and re-think which story you think the data tells, change / add to your list of tables and figures
5. Do the next step of data analysis: play around with your data and create additional tables/figures based on the story that your data seems to tell, make sure to also play around with different ways of visualization

1. Finalize your data analysis: create the final tables and visualizations that tell the story in the best way
2. Choose which tables and figures to include and in which order
3. Critically assess the way that you have presented your data: is it all correct, is it easy to understand, is there a story to be told?
4. Draw conclusions, the story to be told is first of all what can be seen from the data and secondly what the following policy recommendations are
5. Present the data in your report and make sure you are easily able to explain the main findings in 1 minute

• Can you apply these 5 steps to your own project?
  • Ask
  • Collect and understand
  • Clean
  • Analyze
  • Tell your story
• Which steps have you taken?
• Which steps have you not yet taken?
• What have you learnt from these 5 steps?

• Importance of vizualization
• In which steps do you use visualizations?
• Exploratory graphs
• Illustrative graphs
• What type of graphs are you going to make?
• What type of graphs will be in your report/presentation?

Source: https://www.autodeskresearch.com/publications/samestats

• Understanding your data
• Describing data
• Detect patterns
• Validation checks on data
• Use for yourself / internally

• Illustrating a conclusion
• Telling a story with the data
• For publication / to share
• Require more effort!
  • Axis titles
  • Labels
  • Colors
  • etc.

• Standard graphics package - in base R
• ggplot2 package

• Not going to use this much
• Very basic package for visualizations
• Often easier + more convenient to use ggplot2!

hist(ChickWeight$weight)

boxplot(weight ~ Diet, data = ChickWeight)

plot(x = ChickWeight$Time, y = ChickWeight$weight)

https://r-graph-gallery.com/

• Many different types of graphs
• Important to choose the most suitable type
• Try different types to see what works for your data

Layered plotting based on the book The Grammar of Graphics by Leland Wilkinsons.

With ggplot2 you

1. provide the data
2. define how to map variables to aesthetics
3. state which geometric object to display
4. (optional) edit the overall theme of the plot

ggplot2 then takes care of the details

ggplot()

ggplot(ChickWeight)

ggplot(ChickWeight,
       aes(x = Time, 
           y = weight))

ggplot(ChickWeight,
       aes(x = Time, 
           y = weight)) +
  geom_point()

ggplot(data = , mapping = aes()) + ()

Add layers with +

Put + at the end of a line

Map aesthetics with aes()

ggplot(ChickWeight,
       aes(x = Time, 
           y = weight)) +
  geom_point() +
  geom_smooth(color = "blue") +
  labs(x = "Time", y = "Weight", title = "Chick weight over time") +
  theme_minimal()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

ggplot(ChickWeight,
       aes(x = Time, 
           y = weight,
           color = Diet)) +
  geom_point(alpha = 0.5) +
  labs(x = "Time", y = "Weight",
       color = "Diet", title = "Chick weight over time by diet") +
  theme_minimal()

There are many different types of plot you could make! Can you think of more?

• x
• y
• size
• colour
• fill
• opacity (alpha)
• linetype
• …

library(mice)     # Boys dataset

## 
## Attaching package: 'mice'

## The following object is masked from 'package:stats':
## 
##     filter

## The following objects are masked from 'package:base':
## 
##     cbind, rbind

glimpse(boys)

## Rows: 748
## Columns: 9
## $ age <dbl> 0.035, 0.038, 0.057, 0.060, 0.062, 0.068, 0.068, 0.071, 0.071, 0.0…
## $ hgt <dbl> 50.1, 53.5, 50.0, 54.5, 57.5, 55.5, 52.5, 53.0, 55.1, 54.5, 58.5, …
## $ wgt <dbl> 3.650, 3.370, 3.140, 4.270, 5.030, 4.655, 3.810, 3.890, 3.880, 4.2…
## $ bmi <dbl> 14.54, 11.77, 12.56, 14.37, 15.21, 15.11, 13.82, 13.84, 12.77, 14.…
## $ hc  <dbl> 33.7, 35.0, 35.2, 36.7, 37.3, 37.0, 34.9, 35.8, 36.8, 38.0, 40.5, …
## $ gen <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phb <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tv  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ reg <fct> south, south, south, south, south, south, south, west, west, east,…

summary(boys)

##       age              hgt              wgt              bmi       
##  Min.   : 0.035   Min.   : 50.00   Min.   :  3.14   Min.   :11.77  
##  1st Qu.: 1.581   1st Qu.: 84.88   1st Qu.: 11.70   1st Qu.:15.90  
##  Median :10.505   Median :147.30   Median : 34.65   Median :17.45  
##  Mean   : 9.159   Mean   :132.15   Mean   : 37.15   Mean   :18.07  
##  3rd Qu.:15.267   3rd Qu.:175.22   3rd Qu.: 59.58   3rd Qu.:19.53  
##  Max.   :21.177   Max.   :198.00   Max.   :117.40   Max.   :31.74  
##                   NA's   :20       NA's   :4        NA's   :21     
##        hc          gen        phb            tv           reg     
##  Min.   :33.70   G1  : 56   P1  : 63   Min.   : 1.00   north: 81  
##  1st Qu.:48.12   G2  : 50   P2  : 40   1st Qu.: 4.00   east :161  
##  Median :53.00   G3  : 22   P3  : 19   Median :12.00   west :239  
##  Mean   :51.51   G4  : 42   P4  : 32   Mean   :11.89   south:191  
##  3rd Qu.:56.00   G5  : 75   P5  : 50   3rd Qu.:20.00   city : 73  
##  Max.   :65.00   NA's:503   P6  : 41   Max.   :25.00   NA's :  3  
##  NA's   :46                 NA's:503   NA's   :522

head(boys, 3)

##      age  hgt  wgt   bmi   hc  gen  phb tv   reg
## 3  0.035 50.1 3.65 14.54 33.7 <NA> <NA> NA south
## 4  0.038 53.5 3.37 11.77 35.0 <NA> <NA> NA south
## 18 0.057 50.0 3.14 12.56 35.2 <NA> <NA> NA south

tail(boys, 3)

##         age   hgt  wgt   bmi   hc  gen  phb tv   reg
## 7447 20.780 193.5 75.4 20.13   NA <NA> <NA> NA  west
## 7451 20.813 189.0 78.0 21.83 59.9 <NA> <NA> NA north
## 7475 21.177 181.8 76.5 23.14   NA <NA> <NA> NA  east

gg <- 
  boys %>% 
  filter(!is.na(reg)) %>% 
  
  ggplot(aes(x      = hgt, 
             y      = wgt, 
             shape  = reg, 
             colour = age)) +
  
  geom_point( alpha = 0.5) +
  
  labs(title  = "Trend for boys",
       x      = "Height", 
       y      = "Weight", 
       shape    = "Region",
       colour = "Age") +
  theme_minimal()

plot(gg)

• geom_point
• geom_bar
• geom_line
• geom_smooth
• geom_histogram
• geom_boxplot
• geom_density
• and more…

facet_wrap() and facet_grid() divide figures into panels.

boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point() +
  geom_smooth() + 
  facet_wrap(~ reg)

Easy with ggsave()

# save as pdf
ggssave("plot.pdf", myplot)

# save as png and specify dimensions 
ggssave("plot.png", myplot, width = 7, height = 5, units="in")

• R for Data Science
• DataCamp: ggplot 2 courses
• ggplot2: Elegant Graphics for Data Analysis (the official ggplot2 book)

• Simplify and focus
• Increase your text size
• Avoid pie charts, use bar charts
• Avoid line drawings, use fill/color
• Stay 2D